ORIGINAL RESEARCH article
Front. Med.
Sec. Intensive Care Medicine and Anesthesiology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1600481
This article is part of the Research TopicDoing More with Less: Neurosurgery Strategies and Tricks of the Trade in the Technological EraView all articles
Development and Validation of a Perioperative Risk Prediction Model for Pressure Ulcers in Neurosurgical Procedures: A Machine Learning Approach with Protocol Compliance Metrics
Provisionally accepted
- 1Henan Provincial People's Hospital, Zhengzhou, Henan Province, China
- 2Sun Yat-sen University, Guangzhou, China
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Background: This study aimed to develop and validate a nomogram for predicting pressure ulcer (PU) incidence in neurosurgical patients to enhance postoperative risk management.: A retrospective analysis of 1,020 patients across four tertiary centers (2005-2025) evaluated 20 variables. Propensity score matching (PSM) addressed confounding, while LASSO regression and machine learning identified predictors. Model performance was assessed via AUC-ROC, C-index, and decision curve analysis. Results: Eight independent predictors of PU were identified: diabetes duration, BMI, albumin, prealbumin, age, hemoglobin, temperature difference, and urinary incontinence. The training set achieved an AUC-ROC of 0.825 (95% CI: 0.797-0.853) with 77% sensitivity and 92% specificity, while the validation set showed an AUC-ROC of 0.800 (95% CI: 0.753-0.847) with 76% sensitivity and 92% specificity. The nomogram demonstrated recalibrated C-indices of 0.833 (training) and 0.826 (validation). Decision curve analysis confirmed significant net benefit across clinical thresholds. Conclusion: This validated nomogram enables early PU risk stratification, facilitating personalized postoperative interventions. Given its high sensitivity and specificity, the model can be integrated into clinical practice to assist in early identification of high-risk patients, thereby improving patient outcomes through timely interventionsThis validated nomogram enables early PU risk stratification, facilitating personalized postoperative interventions.
Keywords: Neurosurgical procedure, nomogram, Pressure injury, predictive model, retrospective analysis
Received: 28 Mar 2025; Accepted: 16 Jun 2025.
Copyright: © 2025 Wang, Yu, Zhi, Shang, Yin, Shan, Li and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Weiguang Yu, Sun Yat-sen University, Guangzhou, China
Kun Shang, Henan Provincial People's Hospital, Zhengzhou, 450000, Henan Province, China
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